{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/texttt-matryoshka-ii-accelerating-effective","title":"$\\texttt{matryoshka}$ II: Accelerating Effective Field Theory Analyses of the Galaxy Power Spectrum","arxiv_id":"2202.07557","date":"2022-02-15","proceeding":null,"authors":["Jamie Donald-McCann","Kazuya Koyama","Florian Beutler"],"abstract":"In this paper we present an extension to the $\\texttt{matryoshka}$ suite of neural-network-based emulators. The new editions have been developed to accelerate EFTofLSS analyses of galaxy power spectrum multipoles in redshift space. They are collectively referred to as the $\\texttt{EFTEMU}$. We test the $\\texttt{EFTEMU}$ at the power spectrum level and achieve a prediction accuracy of better than 1\\% with BOSS-like bias parameters and counterterms on scales $0.001\\ h\\ \\mathrm{Mpc}^{-1} \\leq k \\leq 0.19\\ h\\ \\mathrm{Mpc}^{-1}$. We also run a series of mock full shape analyses to test the performance of the $\\texttt{EFTEMU}$ when carrying out parameter inference. Through these mock analyses we verify that the $\\texttt{EFTEMU}$ recovers the true cosmology within $1\\sigma$ at several redshifts ($z=[0.38,0.51,0.61]$), and with several noise levels (the most stringent of which is Gaussian covariance associated with a volume of $5000^3 \\ \\mathrm{Mpc}^3 \\ h^{-3}$). We compare the mock inference results from the $\\texttt{EFTEMU}$ to those obtained with a fully analytic EFTofLSS model and again find no significant bias, whilst speeding up the inference by three orders of magnitude. The $\\texttt{EFTEMU}$ is publicly available as part of the $\\texttt{matryoshka}$ $\\texttt{Python}$ package.","url_abs":"https://arxiv.org/abs/2202.07557v2","url_pdf":"https://arxiv.org/pdf/2202.07557v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"texttt-matryoshka-ii-accelerating-effective","repo_url":"https://github.com/jdonaldm/matryoshka_ii_paper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"texttt-matryoshka-ii-accelerating-effective","repo_url":"https://github.com/JDonaldM/Matryoshka","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}